\section{Conclusion}
\label{sec:con}

Much work has focused on mining evolving data, and most approaches
learn the latest model from the latest data.  The problem with these
approaches is that the learned model is always of low quality. In this
paper, we propose a clustering approach to find hidden concepts that
control data generation. Unlike traditional clustering methods that
are based on data similarity (measured by Euclidean distance, e.g.),
we devise a new similarity metric for concept similarity. We propose a
two step algorithm, which uses dynamic programming and hierarchical
clustering to find concepts in the data.%%   Experiments show that in benchmark datasets, our approach
%% achieves the highest accuracy with lowest cost in comparison with the
%% current best approaches.


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